Update CC6204-Hackaton-Cub-Dataset.py

CC6204-Hackaton-Cub-Dataset.py

@@ -10,6 +10,8 @@ from requests import get
 datasets.logging.set_verbosity_info()
 logger = datasets.logging.get_logger(__name__)
 
+_CITATION = "XYZ"
+_HOMEPAGE = "https://github.com/ivansipiran/CC6204-Deep-Learning/blob/main/Hackaton/hackaton.md"
 
 _REPO = "https://huggingface.co/datasets/alkzar90/CC6204-Hackaton-Cub-Dataset/resolve/main/data"
 
@@ -18,8 +20,8 @@ _URLS = {
    "classes": f"{_REPO}/classes.txt",
    "image_class_labels": f"{_REPO}/image_class_labels.txt",
    "images": f"{_REPO}/images.txt",
-   "image_url": f"{_REPO}/images.zip",
-   "text_url": f"{_REPO}/text.zip",
+   "image_urls": f"{_REPO}/images.zip",
+   "text_urls": f"{_REPO}/text.zip",
 }
 
 # Create id-to-label dictionary using the classes file
@@ -27,10 +29,79 @@ classes = get(_URLS['classes']).iter_lines()
 logger.info(f"classes: {classes}")
 
 _ID2LABEL = {}
-for row in classes:
+for row.decode("UTF8") in classes:
    if row != '':
       idx, label = row.split(" ")
       classes[int(idx)] = re.search("[^\d\.\_+].+", label).group(0).replace("_", " ")
       
 logger.info(f"_ID2LABEL: {_ID2LABEL}")
 
+# build from images.txt: a mapping from image_file_name -> id
+_IMGPATH2ID = {}
+
+
+class CubDataset(datasets.GeneratorBasedBuilder):
+   """Cub Dataset"""
+   
+   def _info(self):
+      features = datasets.Features({
+         "image": datasets.Image(),
+         "labels": datasets.features.ClassLabel(names=_NAMES),
+      })
+      keys = ("image", "labels")
+      
+      return datasets.DatasetInfo(
+         description=_DESCRIPTION,
+         features=features,
+         supervised_keys=keys,
+         homepage=_HOMEPAGE,
+         citation=_CITATION,
+      )
+      
+      
+   def _split_generators(self, dl_manager):
+      # 1: train, 0: test
+      train_test_split = get(_URLS["train_test_split"]).iter_lines()
+      train_images_idx = set([x.decode("UTF8").split(" ")[0] for x in train_test_split if x.decode("UTF8").split(" ")[1] == 1])
+      logger.info(f"train_images_idx length: {len(train_images_idx)}")
+      
+      train_files = []
+      test_files = []
+      
+      # Download images
+      data_files = dl_manager.download_and_extract(_URLS["image_urls"])
+      
+      for batch in data_files:
+         path_files = dl_manager.iter_files(batch)
+         for img in path_files:
+            if _IMGPATH2ID[os.path.basename(img)] in train_images_idx:
+               train_files.append(img)
+            else:
+               test_files.append(img)
+               
+      return [
+                 datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                       "files": train_files
+                    }
+                 ),
+                 datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                       "files": test_files
+                    }
+                 )
+      ]
+      
+      
+   def _generate_examples(self, files):
+   
+      for i, path in enumerate(files):
+         file_name = os.path.basename(path)
+         if file_name.endswith(".jpg"):
+            yield i, {
+               "image": path,
+               "labels": os.path.basename(os.path.dirname(path)).lower(),
+            }
+